The Multi-touch Attribution Checklist:  7 Foundational Questions Nobody's Asking before purchasing

The problem isn’t the tool. The issue is more likely to stem from the current state of your CRM, the alignment between sales and marketing on lead sourcing, marketing influence, and where your data is centralized.

Choosing the right attribution model isn’t the starting point. Selecting a model happens after answering key questions about your org’s readiness to start seeking value from such a tool.

This checklist can’t fix broken data, misaligned teams, or missing touchpoints; it shines a light on areas that too many teams rush to fix after implementation.

Start by assessing your current org. Use this checklist to pinpoint what you need to improve so you can start seeing the value as soon as you make the purchase.

What happens when you jump the gun

Let’s talk about what happens when companies skip the self-assessment.

Here’s a common example. A SaaS company scaling across Europe implemented an attribution platform to justify marketing spend. They had the budget. They had buy-in from leadership. They had a roughly year-long sales cycle with dozens of touchpoints across paid ads, content, webinars, and sales outreach.

The need for an attribution solution was clear. What wasn’t clear was :

• campaign tagging that was inconsistent
• lead source data for leads linked to deals and opportunities
• alignment between sales and marketing on what “generated pipeline” meant and the content’s impact.

The result: huge revenue discrepancies between campaigns and teams. This very real example saw LinkedIn ads credited with $2.3M in pipeline by the attribution platform, but $890K in Salesforce. Marketing swore by the number; sales said otherwise. The $1.41M discrepancy exposed an expensive misalignment.

Trust in the data evaporated.

This technology wasn’t the issue. The problem stemmed from the company's attempt to build attribution insights on top of a data infrastructure that couldn’t support them.

Data infrastructure comes before data insights. Always.

Clean CRM data. Consistent tracking. Aligned definitions. Reconciliation processes. These aren’t nice-to-haves. They’re prerequisites. What data would make you trust marketing? As a marketing leader or someone taking on attribution in your organization, thinking about this question helps you foster shared ownership and accelerate alignment conversations.

Which brings us to the framework.

The 7 Attribution Readiness Questions Framework

If you can confidently answer “yes” to all seven, bravo! You have the infrastructure in place for accurate multi-touch attribution. If your answer is “no” or “maybe,” there are a few things to address before picking the right attribution tool.

Question 1: Can You Trust Your CRM Data Today?

Here’s a quick test. Pull up Salesforce or HubSpot right now. Look at your most recent 20 closed-won opportunities. Check the lead source field, the campaign associations, and the contact roles.

Are you seeing incomplete data? How many are “unknown” or have “other” as the lead source? How many opportunities have zero campaign associations, despite being clearly influenced by marketing?

If you don’t have your CRM data in order, you're shooting yourself in the foot by adding an added level of complexity with an attribution solution.

Nothing about CRM hygiene is exciting. It’s repetitive and unglamorous work. It’s the marketing ops equivalent of flossing. Everyone knows they should do it. Almost nobody does it consistently.

But without it, your attribution insights are not trustworthy. If sales don’t trust the lead source field today, they definitely won’t trust  what your multi-touch attribution solution credits tomorrow.

The CRM Data Checklist:

• UTM Campaign tagging: Do you have a clear and consistent UTM parameter process across channels? Can you trace links from a guest blog post click, a webinar sign-up page, a LinkedIn ad, and a newsletter using the same nomenclature?
• Lead source accuracy: Can you reliably identify the source of leads? Not just “paid social” but which specific campaign, which ad set, which piece of creative?
• Opportunity associations: Are opportunities in your CRM linked to the campaigns that influenced them? Or do most opps sit with no campaign history?
• Duplicate detection: Can you confidently say you’re not double-counting the same person across multiple records? Because if you are, your attributionis already off.
• Offline integration: Do trade show leads get entered consistently? Are sales call notes actually logged? Can you connect a conference meeting to a closed deal six months later?

If you answered no to any of these, spend time cleaning your CRM. You’ll need to :

• Remove duplicates
• Standardize lead sources
• Use consistent UTM parameters
• Check CRM fields
• Improve offline tracking.

Start by clarifying the following steps: lead source cleanup, building a campaign taxonomy that both marketing and sales understand, training the team on opportunity association, and setting up automated alerts for missing essential fields.

It’s not glamorous, but it’s essential. If your CRM data isn’t clean, attribution just creates more ways to get it wrong.

That’s why automating as much of this process as possible is important. It means less human error and more time spent using data from your CRM rather than wasting hours cleaning it. Implementing automations or integrating a native solution that ensures contacts are associated with deals and campaigns helps your attribution system link campaigns to leads that generate revenue.

Question 2: Do Marketing and Sales Agree on What “Revenue Impact” Means?

Attribution doesn’t magically align marketing and sales on what impacts revenue. More often than not, it shines a light on the gap between the two teams.

Here’s a common example of how misalignment on revenue impact plays out: Marketing says they “influenced” $5M in pipeline. Sales points to marketing having “sourced” $1.2M. Finance looks at both numbers and asks which one actually matters for the budget conversation.

Neither team has an answer because nobody defined the terms before implementing attribution.

This isn’t a data problem. It’s a definition problem. And it needs to be solved before you add multi-touch attribution to the mix.

The questions you need aligned answers to:

• What’s an MQL? It’s not what some guide told you is an MQL. You need a definition, based on your ICP and win-loss analysis, that determines when sales are notified and what they’re expected to do about it.
• What’s SQL? Who decides when an MQL is ready to be pushed to sales? What are the criteria? How are leads being scored? Automatically? Manually? Or some combination?
• What does “pipeline generated” mean? Is it the first campaign touch? Is the campaign associated with the opportunity? Any campaign that touched an account within 90 days of opportunity creation?
• [What about “influenced” opportunities? Do they count? How much? If marketing touches an existing sales-sourced opportunity, does that show up in marketing’s attribution?
• Is there a shared revenue goal or separate targets? If marketing has a “pipeline influenced” target and sales has a “revenue closed” target with no connection between the two, attribution creates more political ammunition instead of alignment.

Consider this: Shared definitions unlock bigger wins for both teams. If sales and marketing pursue different definitions of success, multi-touch attribution will make this alignment gap apparent to all, including leadership.

The Marketing and Sales Alignment Assessment:

Schedule an alignment meeting and put these questions on a whiteboard:

• What is defined as pipeline generation?
• How do we define pipeline influence?
• How should attribution credit work for opportunities that sales sourced but marketing nurtured?
• What happens when an account engages with marketing after a sales rep already started outreach?

This is how you ensure everybody is clear on how their work is credited.

That’s why a CRM-native approach matters. Attribution data stored in your CRM drives alignment. When sales sees marketing touchpoints directly on opportunity records, and both teams are looking at the same data, the dynamic moves from “us vs. you” to a focus on a common goal.

External attribution dashboards that marketing controls? That widens the gap between teams.

CRM-native attribution? That builds alignment in a tool that everyone already uses.

Question 3: Are You Tracking Offline Touchpoints?

With your newfound sales and marketing alignment and cleaned data in place, we’re off to the next obstacle: offline touchpoint tracking.

Your prospects don’t live entirely online. They attend trade shows. They take sales calls. They receive direct mail. They listen to podcasts. They sit through in-person demos and executive briefings.

If you're not accurately tracking offline interactions, your attribution model is missing half the story.

When someone stops by your booth at a conference, what happens? Do sales reps scan their badge, manually enter the new lead into the CRM, or collect business cards that may get entered later? Can you link booth visits to the webinar they attended two weeks earlier and the demo they had three weeks later?

Here’s the pattern we see: B2B companies get pretty good at tracking online touchpoints. They’ve got pixels firing, UTM parameters, whether they’re consistent, and form submissions tracked. But the offline side of the equation? It’s often inconsistent.

If your attribution solution doesn’t accurately credit offline touches, it can’t determine what’s driving leads to a final sale, especially in companies with lengthy discussions and sales processes.

Reality Check Questions:

• Event tracking: Do you have a standardized process for getting event leads into your CRM within 48 hours?
• Sales activity logging: Does your sales team consistently log calls, meetings, and demos in the CRM?
• Executive engagement: When your CEO meets with a prospect for coffee, is that recorded anywhere? When your product team conducts a technical deep dive, is it logged?

If the answer to most of these is “sometimes,” fix this before implementing multi-touch attribution.

Start with the highest-value offline touchpoints:

• Sales calls and demos (use call recording tools with CRM integration)
• Trade shows and events (standardize the badge scan-to-CRM workflow)
• Executive engagement (create simple Slack-to-CRM shortcuts)

Without consistent tracking of the interactions that actually move deals, your attribution model will unfairly point to digital channels when, in reality, sales is closing deals that marketing helped generate.

Question 4: Do You Have a Budget to Defend Marketing Spend This Quarter?

If you’re already underwater fighting for budget, if you’re walking into next quarter’s planning meeting needing to justify every dollar or face cuts, multi-touch attribution probably won’t save you fast enough.

Here’s why: sophisticated attribution models need data volume to be accurate. You need multiple sales cycles’ worth of touchpoint data. You need enough closed deals to validate that your attribution weighting actually correlates with revenue outcomes.

For most B2B companies with 3-6 month sales cycles, that means 90-180 days of data collection before you have statistically useful insights.

Can you wait that long?

If the answer is no, if you need to prove marketing’s value right now, then start simpler. First-touch and last-touch attribution can be set up in a week. They’re not perfect, but they give you immediate talking points. You can show which campaigns started relationships (first-touch) and which ones closed deals (last-touch).

That buys you time. Then, while you’re using those simple models to defend the current budget, you can build toward more sophisticated multi-touch attribution for next quarter’s planning cycle.

Do you have 90+ days of runway to collect data before needing attribution results?

If yes: invest in building a proper multi-touch attribution system. Take the time to get the data infrastructure right, align your teams, and choose the appropriate model for your sales cycle.

If no: Start with simple attribution now to defend the current budget. Plan your multi-touch implementation for next quarter. Don’t let perfect be the enemy of good enough when you’re under immediate pressure.

Fix that first. Then graduate to multi-touch once you have clean data and a stable budget to support the transition.

Time-decay models, W-shaped attribution, and custom algorithmic weighting—all of these require data maturity that takes time to build. If you’re in budget defence mode right now, acknowledge that reality. Get quick wins with simple models. Build toward sophistication when you have the luxury of time.

Question 5: Does Your Team Understand Weighting?

Everyone who will use the insights to make decisions needs to understand how they are derived.

Here’s the test: Can your demand gen manager explain to the CMO what “30% time-weighted credit” means and why it matters? Can your CMO then explain it to the board in a way that builds confidence rather than confusion?

If not, you have an education problem.

We’ve seen companies implement sophisticated, custom attribution models with algorithmically driven weighting and machine learning. Beautiful dashboards. Mathematically sound models. Completely unused.

Why? When the VP of Marketing presented the insights to the executive team, no one understood how the numbers were calculated. They couldn’t explain why LinkedIn received 23% credit rather than 18%. They couldn’t defend the model when finance asked pointed questions.

This is how distrust can replace insights.

Readiness Check:

Explanation test: Can your marketing team explain each attribution model option to a smart 12-year-old? If you can’t simplify the concept, you don’t understand it well enough to implement it.

QA capability: Is there someone on your team who can spot-check attribution math? When the model says a campaign generated $500K in pipeline, can someone trace that back to individual opportunities and validate the calculation?

Behaviour change: Will stakeholders actually change their decisions based on attribution data? Or will they nod during the presentation and then keep doing what they’ve always done?

If you’re hitting “no” on several of these, you need to build analytical capability before implementing complex attribution.

Start with education. Run lunch-and-learns on attribution concepts. Walk through examples using your actual data. Show what linear attribution would reveal versus time-decay versus position-based. Let people see the differences and ask questions.

Then start simple. You may want to implement a basic multi-touch model; linear attribution is the easiest to explain. Every touchpoint gets equal credit. Simple math. Easy to understand.

Run that for a quarter. Let the team get comfortable with the concept. Let them start asking questions like “Should we really weigh all touchpoints equally?”

That’s when you know they’re ready for more sophistication.

Sophistication ought to follow demand, not precede it. Build the analytical literacy first.

Question 6: Are You Measuring Account-Level Engagement or Just Leads?

Here’s where most lead-based attribution falls apart in B2B: buying committees.

A technical evaluator downloads your whitepaper. The VP of Engineering attends your webinar. The CTO joins a product demo. The CFO reviews your pricing page. The CEO has coffee with your sales rep.

That’s five different people, five different touchpoints, one buying decision.

If your attribution model only looks at individual lead behaviour, you’re missing the account-level patterns that actually predict deal success.

ABM Questions:

Multiple contacts: Do you track multiple contacts per account in your CRM? Not just the primary contact, but the entire buying committee?

Cross-role patterns: Can you analyze engagement by role? Can you see that technical evaluators engage with product documentation as executives engage with ROI calculators and case studies?

Account-based views: Can you see all touchpoints across all contacts at an account, not just individual lead histories?

Buying committee identification: Can you identify when you have coverage across a buying committee versus just one champion?

If you can’t answer yes to these questions, your attribution model is structurally limited for B2B. You’re trying to understand account-based buying with lead-based data.

Here’s what that looks like in practice:

Lead-based attribution says: “This person downloaded an ebook, attended a webinar, and requested a demo. They became an opportunity 30 days later. Those three touchpoints get attribution credit.”

Account-based attribution says: “This account had seven people engage across fourteen touchpoints. The technical evaluator downloaded documentation. The business owner attended a webinar. The exec sponsor had a sales call. The procurement team reviewed pricing. All of those interactions contributed to the buying committee’s decision. Here’s how we weight them.”

See the difference? The second view conveys the complexity of B2B buying.

And here’s the CRM-native advantage: when your attribution data lives in Salesforce or HubSpot, it already exists in an account-based structure. Contacts roll up to accounts. Opportunities belong to accounts. The data model naturally supports account-based attribution.

External attribution platforms usually struggle with this. They’re built around individual user tracking done with cookies, device IDs, and behavioural sequences. Trying to retrofit account-based logic onto user-based tracking is messy.

Question 7: Can You Reconcile Attribution Credits to Actual Revenue?

This is the question that separates attribution theatre from attribution reality.

Pull your most recent attribution report. Add up all the attribution credits across all channels. Now compare that total to your actual closed-won revenue for the same period.

Do the numbers match? If not, your attribution math is broken.

Here’s what usually happens: attribution models over-credit. They tally the same deal multiple times because different channels each take full or partial credit. Without reconciliation, you end up reporting that marketing generated $2M in revenue when actual closed deals totaled $1.2M.

That’s not just mathematically wrong. It’s politically dangerous. When finance audits those numbers and finds the discrepancy, they’ll question every attribution insight you’ve ever presented.

The Reconciliation Test:

Total credit = Total revenue: For any given time period, the sum of all attribution credits across all touchpoints should equal your actual closed-won revenue. Not more, not less. Exactly equal.

Opportunity-level validation: Pick ten recent closed deals. For each one, add up the attribution credits across all touchpoints. The total should equal the deal value. Then multiply out—if this works for ten deals, it should work for all deals.

Cross-functional verification: Can revenue operations or finance validate your attribution methodology? Do they sign off on the math?

Audit trail: Can you drill into any attributed revenue number and show exactly which opportunities contributed? Which touchpoints received credit? Why did they receive that specific amount of credit?

If you can’t pass these tests, you don’t have attribution. You have creative accounting.

This is where many attribution platforms fail in practice. They make it easy to assign credit to touchpoints. They make pretty dashboards showing channel performance. But they don’t enforce reconciliation discipline.

You can end up with multiple “sources of truth” instead of one. Marketing’s attribution dashboard shows one set of numbers. The CRM shows different numbers. Finance’s revenue reports show a third set. Nobody trusts anybody’s data.

Reconciliation discipline isn’t optional. It’s fundamental.

Build it into your process from day one. Set up monthly attribution audits. Verify that total attributed revenue equals actual revenue. When it doesn’t match, investigate why and fix it.

Common reconciliation errors:

• Double-counting deals when multiple contacts at the same account convert
• Attributing to touchpoints that happened after the deal closed
• Overlooking the need to account for deal value changes during the sales cycle
• Not handling multi-year contracts consistently.

None of these is an unsolvable problem. But they require process, rigour, and cross-team collaboration to fix.